Field of the Invention
The present invention relates to a technique of recognizing the category of an object in an image and its region.
Description of the Related Art
Techniques of discriminating regions according to the categories of objects are called semantic region division. The conventional semantic region division methods include the following two types of techniques. The first technique discriminates the category of an object in each local region based on the feature of the local region. As the second technique, Non Patent Literature 1 (Lubor Ladicky, Paul Sturgess, Karteek Alahari, Chris Russell, and Philip H. S. Torr, What, Where & How Many? Combining Object Detectors and CRFs, ECCV 2010) describes a technique that improves the accuracy of region division by adopting a technique of detecting a specific object in combination with the first technique. The method of Non Patent Literature 1, first, estimates a range in which an object resides, based on an object detection result. Next, this method raises the likelihood of the category of the object by a predetermined value during discrimination of the local region, because the likelihood of being the object in the region in the range is higher than the likelihoods in the other regions.
However, the position and range of the object that can be estimated from a detection result of the object contain errors. Consequently, if the likelihood is uniformly high in the estimated range, the accuracy of the boundary of the obtained region division result is low. To improve the accuracy of the boundary, Non Patent Literature 1 takes the following measures. That is, first, the range of the object is divided into two regions that are a foreground and a background, based on the color feature. Next, since the local region estimated as a foreground has a higher likelihood of being the object than the other regions, the likelihood of the category of the object is raised by a predetermined value only in the local region having been estimated as the foreground, and discrimination is performed.
Unfortunately, if the background region has a color similar to that of the detected object, this method sometimes erroneously discriminates the foreground and background. A complicated background and overlapping objects may also lead to mis-discrimination. That is, according to the method that identifies the foreground of the object and uniformly changes the likelihood, it is sometimes difficult to discriminate a certain object.
The present invention has been made in view of the above points, and has an object to improve the accuracy of recognizing the category of the object and its region in the image.